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A cloud-based pipeline for analysis of FHIR and long-read data.
Dunn, Tim; Cosgun, Erdal.
Affiliation
  • Dunn T; Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
  • Cosgun E; Biomedical Platforms and Genomics, Microsoft Research, Redmond, WA 98052, USA.
Bioinform Adv ; 3(1): vbac095, 2023.
Article in En | MEDLINE | ID: mdl-36726729
ABSTRACT
Motivation As genome sequencing becomes cheaper and more accurate, it is becoming increasingly viable to merge this data with electronic health information to inform clinical decisions.

Results:

In this work, we demonstrate a full pipeline for working with both PacBio sequencing data and clinical FHIR® data, from initial data to tertiary analysis. The electronic health records are stored in FHIR® (Fast Healthcare Interoperability Resource) format, the current leading standard for healthcare data exchange. For the genomic data, we perform variant calling on long-read PacBio HiFi data using Cromwell on Azure. Both data formats are parsed, processed and merged in a single scalable pipeline which securely performs tertiary analyses using cloud-based Jupyter notebooks. We include three example applications exporting patient information to a database, clustering patients and performing a simple pharmacogenomic study. Availability and implementation https//github.com/microsoft/genomicsnotebook/tree/main/fhirgenomics. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Full text: 1 Collection: 01-internacional Health context: 1_ASSA2030 Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Bioinform Adv Year: 2023 Document type: Article

Full text: 1 Collection: 01-internacional Health context: 1_ASSA2030 Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Bioinform Adv Year: 2023 Document type: Article